Function Of Standard Deviation Research Articles

Spatial distribution of soil rare earth elements in Sicily (Italy)

Rare earth elements (REEs) are becoming increasingly interesting as indicators of soil processes, and modelling their spatial distribution has become a fundamental requirement for this purpose. This study aims to model and quantify the spatial distribution of soil REEs taking into account their compositional nature in their mapping, and to assess the associated spatial uncertainty. In particular, the cerium anomaly (Ce⁎) and the ratio of the sum (Σ) of light to heavy REEs (ΣLREEs/ΣHREEs) were calculated and analysed. The study was carried out in Sicily (Italy), one of the largest (25,832 km2) and most populous island in the Mediterranean Sea. Soil REE data were obtained from the soil database of Sicily region. Soil REEs were transformed into a vector of isometric log-ratio (ilr) coordinates and analysed using a geostatistical approach for predicting their values at unsampled locations and generating 100 REEs realizations using turning bands simulation. Each REE element and Ce⁎ were mapped as well as the ΣLREEs/ΣHREEs ratio. The joint variability of the investigated REEs and the ΣLREEs/ΣHREEs ratio provided insights into their abundance and distribution patterns. From the simulated realizations, standard deviation maps were calculated for the Ce⁎ and the ΣLREEs/ΣHREEs ratio, providing a measure of their spatial uncertainty. Although with some limitations, the study established a first baseline of the Ce⁎ and the ΣLREEs/ΣHREEs ratio. The maps of spatial uncertainty may be a useful tool for planning future soil sampling and optimise the choice of new sampling locations.

A Framework for Conducting and Communicating Probabilistic Wildland Fire Forecasts

Fire models predict fire behavior and effects. However, there is a need to know how confident users can be in forecasts. This work developed a probabilistic methodology based on ensemble simulations that incorporated uncertainty in weather, fuel loading, and model physics parameters. It provided information on the most likely forecast scenario, confidence levels, and potential outliers. It also introduced novel ways to communicate uncertainty in calculation and graphical representation and applied this to diverse wildfires using ensemble simulations of the CAWFE coupled weather–fire model ranging from 12 to 26 members. The ensembles captured many features but spread was narrower than expected, especially with varying weather and fuel inputs, suggesting errors may not be easily mitigated by improving input data. Varying physics parameters created a wider spread, including identifying an outlier, underscoring modeling knowledge gaps. Uncertainty was communicated using burn probability, spread rate, and heat flux, a fire intensity metric related to burn severity. Despite limited ensemble spread, maps of mean and standard deviation exposed event times and locations where fire behavior was more uncertain, requiring more management or observations. Interpretability was enhanced by replacing traditional hot–cold color palettes with ones that accommodate the vision-impaired and adhere to web accessibility standards.

Deep learning‐based low‐dose CT simulator for non‐linear reconstruction methods

AbstractBackgroundComputer algorithms that simulate lower‐doses computed tomography (CT) images from clinical‐dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image‐domain noise simulation methods useful. However, the introduction of non‐linear reconstruction methods, such as iterative and deep learning‐based reconstruction, makes noise insertion in the image domain intractable, as it is not possible to determine the noise textures analytically.PurposeTo develop a deep learning‐based image‐domain method to generate low‐dose CT images from clinical‐dose CT (CDCT) images for non‐linear reconstruction methods.MethodsWe propose a fully image domain‐based method, utilizing a series of three convolutional neural networks (CNNs), which, respectively, denoise CDCT images, predict the standard deviation map of the low‐dose image, and generate the noise power spectra (NPS) of local patches throughout the low‐dose image. All three models have U‐net‐based architectures and are partly or fully three‐dimensional. As a use case for this study and with no loss of generality, we use paired low‐dose and clinical‐dose brain CT scans. A dataset of paired scans was retrospectively obtained. All images were acquired with a wide‐area detector clinical system and reconstructed using its standard clinical iterative algorithm. Each pair was registered using rigid registration to correct for motion between acquisitions. The data was randomly partitioned into training ( samples), validation ( samples), and test ( samples) sets. The performance of each of these three CNNs was validated separately. For the denoising CNN, the local standard deviation decrease, and bias were determined. For the standard deviation map CNN, the real and estimated standard deviations were compared locally. Finally, for the NPS CNN, the NPS of the synthetic and real low‐dose noise were compared inside and outside the skull. Two proof‐of‐concept denoising studies were performed to determine if the performance of a CNN‐ or a gradient‐based denoising filter on the synthetic low‐dose data versus real data differed.ResultsThe denoising network had a median decrease in noise in the cerebrospinal fluid by a factor of and introduced a median bias of HU. The network for standard deviation map estimation had a median error of HU. The noise power spectrum estimation network was able to capture the anisotropic and shift‐variant nature of the noise structure by showing good agreement between the synthetic and real low‐dose noise and their corresponding power spectra. The two proof of concept denoising studies showed only minimal difference in standard deviation improvement ratio between the synthetic and real low‐dose CT images with the median difference between the two being 0.0 and +0.05 for the CNN‐ and gradient‐based filter, respectively.ConclusionThe proposed method demonstrated good performance in generating synthetic low‐dose brain CT scans without access to the projection data or to the reconstruction method. This method can generate multiple low‐dose image realizations from one clinical‐dose image, so it is useful for validation, optimization, and repeatability studies of image‐processing algorithms.

Stability assessment of immunobiological medicinal products manufacturing for veterinary use with Shewhart control charts

According to the current Rules of Good Manufacturing Practice of the Eurasian Economic Union, the principles of risk management for product quality must be applied to all aspects of the production and use of medicines for medical and veterinary purposes. The recommended quality risk management method used by regulators is the Shewhart Chart. Control charts, proposed in 1924 by W. Shewhart, are a graphical tool for applying statistical principles to process control. Statistical process control is a methodology for establishing and maintaining production at an acceptable and stable level, providing the required product quality. Control charts are used to collect data during the continuous recording of the characteristics of the quality of manufactured products. As a result of continuous analysis of information, control charts help to identify unusual patterns of data variation and take preventive measures to eliminate them and increase process stability. The use of Shewhart maps leads to a more detailed understanding of the process and helps to discover ways for valuable improvements. The purpose of the work is to assess the stability of the production of immunobiological medicinal products for veterinary use using Shewhart's control charts. The immunobiological drug "Oralrabivak" produced at the Schelkovo Biokombinat FKP was used as a model object. We used control charts of individual values (X-chart) and moving ranges (Rm-chart) and maps of mean values (-chart) and sample standard deviations (s-chart). The assessment of the stability of the production of Oralrabivak using Shewhart's control charts showed that during two production cycles (2021 and 2022) the technological process was in a state of statistical controllability. To improve the process and ensure the quality of the finished product, the specialists of the enterprise have taken preventive measures. The methodology of control charts made it possible to visualize and study in detail a new production process for the enterprise for a deeper understanding of its features by the specialists of the enterprise and further improvement.

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

The accurate calculation of ore rock fragmentation is important for achieving the autonomous mining operation of mine excavators. However, a single mode cannot accurately calculate the ore rock fragmentation due to the low resolution of the point cloud mode and the lack of spatial position information of the image mode. To solve this problem, we propose an ore rock fragmentation calculation method (ORFCM) based on the multi-modal fusion of point clouds and images. The ORFCM makes full use of the advantages of multi-modal data, including the fine-grained object segmentation of images and spatial location information of point clouds. To solve the problem of image under-segmentation, we propose a multiscale adaptive edge-detection method based on an innovative standard deviation map to enhance the weak edges. Furthermore, an improved marked watershed segmentation algorithm is proposed to solve the problem of low segmentation accuracy caused by excessive noise of the gradient map and weak edges submerged. Experiments demonstrate that ORFCM can accurately calculate ore rock fragmentation in the local excavation area without relying on external markers for pixel calibration. The average error of the equivalent diameter of ore rock blocks is 0.66 cm, the average error of the elliptical long diameter is 1.42 cm, and the average error of the elliptical short diameter is 1.06 cm, which can effectively meet practical engineering needs.

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

Plunging breakers. Part 1. Analysis of an ensemble of wave profiles

An experimental study of the dynamics and droplet production in three mechanically generated plunging breaking waves is presented in this two-part paper. In the present paper (Part 1), the dynamics of the three breakers are studied through measurements of the evolution of their free surface profiles during 10 repeated breaking events for each wave. The waves are created from dispersively focused wave packets generated with three wave maker motions that differ primarily by small changes in their overall amplitude. Breaker profiles are measured with a cinematic laser-induced fluorescence technique covering a streamwise region of approximately one breaker wavelength and over a time of 3.4 breaker periods. The aligned profile data is used to create spatio-temporal maps of the ensemble average surface height and the standard deviation of both the local normal distance and the local arc length relative to the instantaneous mean profile. It is found that the mean and standard deviation maps contain strongly correlated localized features and indicate that the transition from laminar to turbulent flow is a highly repeatable process. Regions of high standard deviation include the splash created by the plunging jet impact and subsequent splash impacts at the front of the breaking region as well as the site where the air pocket entrained under the plunging jet at the moment of jet tip impact comes to the surface and pops. In Part 2 (Erinin et al., J. Fluid Mech., vol. 967, 2023, A36), these features are used to interpret various features of the distributions of droplet number, diameter and velocity.

MOJAVE – XXI. Decade-long linear polarization variability in AGN jets at parsec scales

ABSTRACT Using stacking of images obtained at different epochs, we studied the variability properties of linear polarization of active galactic nucleus (AGN) jets on parsec-scales. Our sample is drawn from the MOJAVE programme, and consists of 436 AGNs manifesting core-jet morphology and having at least five VLBA observing epochs at 15 GHz from 1996 January through 2019 August, with some additional archival VLBA data reduced by us. We employed a stacking procedure and constructed maps of (i) standard deviation of fractional polarization and electric vector position angle (EVPA) over epochs as the measure of variability and (ii) median polarization degree to quantify typical values in time. The distributions of these values along and across the jet were analysed for the whole sample for the first time. We found that core EVPA variability is typically higher than that of the jet, presumably due to component blending and outflow bends in the core. The BL Lacertae object cores have lower EVPA variability, compared to that of quasars, possibly due to lower Faraday rotation measure, suggesting a stronger ordered magnetic field component. The EVPA becomes more stable down the jet. Most of the sources showing this trend have a time coverage of more than 12 yr and at least 15 epochs. The possible cause could be the increase of stability in the magnetic field direction, reflecting an increase in the fraction of the magnetic field that is ordered. There are no significant optical-class-dependent or spectral-class-dependent relations in the EVPA variability properties in AGN jets.

MRI tagging of colonic chyme mixing in healthy subjects: Inter-observer variability and reliability of the measurement with time.

Magnetic resonance imaging (MRI) tagging techniques have been applied to the GI tract to assess bowel contractions and content mixing. We aimed to evaluate the dependence of a tagging measurement (for assessing chyme mixing) on inter-observer variability in both the ascending colon (AC) and descending colon (DC) and to investigate the temporal variation and hence reliability of the colonic tagging technique by acquiring multiple measurements over time on healthy participants. Two independent datasets of healthy adults were used for the retrospective inter-observer variability (Study 1: 13 datasets and Study 2: 31 datasets), and ten participants were scanned for the prospective temporal variation study following a 1 L mannitol oral preparation. All colonic tagging data were acquired on 3 T MRI scanners. The mean and the standard deviation (SD) maps were generated pixel-by-pixel using custom-written software in MATLAB. The colonic regions of interest were defined using MIPAV software. Bland-Altman plots and scatter plots were used for the inter-observer variability. The mean and SD of all repeated measures for each subject were calculated along with a one-way ANOVA to test for variations with time. Scatter plots and Bland-Altman plots showed a large range of data with low variation and small limits of agreements (<5% CoV). The intraclass correlation coefficient of inter-rater reliability was excellent and 0.97 or above for the AC and DC measurements for both datasets. The temporal variation study shows that there was no significant difference found between the multiple measures with time (p = 0.53, one-way repeated measures ANOVA test). MRI tagging technique can provide an assessment of colonic chyme mixing. The inter-observer study data showed high inter-rater agreement. The temporal variation study showed some individual variations with time suggesting multiple measurements may be needed to increase accuracy.

Time-correlated luminescence blinking in InGaN single quantum wells

The blinking phenomenon in InGaN single quantum wells is a phenomenon where localized photoluminescence changes over time. Understanding its physics is important for the manufacture of more efficient light emission diodes. We present a study using two InGaN single quantum well samples, emitting at 460 and 510 nm wavelength, respectively. We confirmed that the luminescence intensity fluctuates in localized blinking regions, and we found that these optical variations are not random but are instead correlated in pairs, with either positive or negative coefficient, to a distant reference blinking point. Measurements were performed to obtain standard deviation and cross-correlation maps. Invoking the quantum confined Stark effect, we realized a simple phenomenological model that shows how charge carriers are exchanged among pairs of adjacent opposite correlation regions. As a result, it is suggested that the phenomenon is caused by fluctuations in the number of these exchanged carriers. Our model gives an explanation for the blinking phenomenon in InGaN single quantum wells, and it is important for a deeper understanding to InGaN-based materials.

Correlation between Central Visual Field Defects and Stereopsis in Patients with Early-to-Moderate Visual Field Loss

To investigate the association between stereoacuity and the presence of central visual field defects (CVFDs) due to glaucoma. A prospective, cross-sectional cohort study. Participants with early-to-moderate glaucoma with a visual acuity better than 20/40, less than a 2-line difference in visual acuity between eyes, and 2 reliable Humphrey visual fields (VFs) (24-2 SITA standard) with mean deviation (MD) in the worse eye better than-12 dB. Stereoacuity was measured using the Titmus stereo test. Participants with a significant field defect (P<0.005) in any 1 of the central 4 points in the 24-2 SITA standard total deviation map in either eye were classified as having a CVFD. Vision-related quality of life (VR-QOL) was measured using 25-item National Eye Institute Visual Function Questionnaire scores. Logistic regression was used to determine the associations between the level of stereoacuity and age, sex, race, glaucoma type, presence of CVFDs, visual acuity, contrast sensitivity, and VF MD. Stereoacuity in the CVFD and non-CVFD groups. Sixty-five participants met the inclusion criteria. The mean age of the participants was 64.3±8.0 years, and 64.6% were women. The median stereoacuity was 60 arc seconds (interquartile range [IQR], 40-120 arc seconds). Forty-two (65%) patients had CVFDs, and 23 (35%) patients did not. The median stereoacuity of the CVFD group was worse than that of the non-CVFD group (60 arc seconds [IQR, 50-140 arc seconds] vs. 40 arc seconds [IQR, 40-80 arc seconds], respectively; P=0.001). The non-CVFD group had a higher percentage of participants with normal stereopsis than the non-CVFD group (61% vs. 21%, respectively; P=0.001). A multivariable analysis found that the presence of CVFDs was associated with worse stereopsis levels (odds ratio, 4.49; P=0.021). The CVFD group had a lower Visual Functioning Questionnaire-25 (VFQ-25) composite score (84.0 vs. 91.4; P=0.004) and lower VFQ-25 subscale scores for general vision, near activities, and mental health (P<0.05). Central visual field defects were associated with increased odds of poor stereoacuity in patients with early-to-moderate glaucomatous VF loss. Specifically, patients without CVFDs are more likely to have normal stereopsis and higher VR-QOL than those with CVFDs. Patients with CVFDs should be counseled regarding how depth perception difficulties may affect daily living. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Implementation of Standard Deviation Map (SDM) for an Automation of Spatial Resolution Measurements on Computed Tomography Images of ACR CT Accreditation Phantom

This study is to develop an automated method to determine the spatial resolution of computed tomography (CT) images on line-pair objects of the American College of Radiology (ACR) CT accreditation phantom. The ACR phantom was scanned using a GE Healthcare 128-slice CT scanner with seven different reconstruction filters of E1, E2, E3, LU, S1, S2, S3. The automated method involved building a standard deviation map (SDM), segmenting the line-pair objects within SDM images, determining the region of interest (ROI) within line pair object, and determining a resolvable line-pair object with dynamic threshold which is dependent on image noise. The scanning parameters were fixed at 120 kVp and 160 mAs. The results of automated method were compared with those from manual measurements performed by five human observers. The automatic method produced spatial resolution results of 0.7, 0.7, 0.7, 0.7, 0.6, 0.6, and 0.6 lp/mm for filters E1, E2, E3, LU, S1, S2, and S3, respectively, while the manual measurements yielded results of 0.6, 0.6, 0.6, 0.7, 0.6, 0.5, and 0.5 lp/mm. The differences between the manual and automatic measurement results were small, with a maximum difference of 0.1 lp/mm. Hence, the automatic measurement of spatial resolution on line-pair objects of the ACR CT accreditation phantom is a feasible and reliable method.

Blind denoising of dental X-ray images

The paper considers a problem of automatic analysis and noise suppression in dental X-Ray images, e.g., in images acquired by dental Morita system. Such images contain spatially correlated noise with unknown spectrum and with standard deviation that varies for different image regions. In the paper, we propose two deep convolutional neural networks. The first network estimates the spectrum and level of noise for each pixel of a noisy image, predicting maps of noise standard deviation for three image scales. The second network uses the maps as inputs to suppress noise in the image. It is shown, using modelled and real-life images, that the proposed networks provide PSNR for dental X-Ray images by 2.7 dB better than other modern denoising methods.

Mach and Froude Numbers on Mars

Mars atmospheric global circulation models exhibit transonic jet streaks during northern winter, which motivates this study of the Mach number, Ma (the ratio of flow speed to the speed of sound), and Froude number, Fr (the ratio of flow speed to the speed of buoyancy waves), as a function of season and location. Two global reanalyses spanning Mars Years (MY) 24 to 32 are used as input, EMARS and OpenMARS. The study’s vertical coordinate is potential temperature, θ, ranging from θ = 400 to 1100 K (from z ≈ 32 to 66 km); the floor is set to the lowest level that avoids intersecting mountains. Area-weighted global means and standard deviations and 5 yr temporal means using the complete years MY 25, 26, 29, 30, and 31 are compared. EMARS and OpenMARS show general agreement below θ = 700 K (z ≈ 53 km), where the observational constraints are strongest, but can vary significantly at higher levels. Both reanalyses contain transonic jet streaks in every northern winter sampled. The Fr signal is roughly twice the Ma number signal, as expected where the temperature lapse rate is small compared to the dry adiabatic lapse rate. Mach numbers are similar in both reanalyses but show larger year-to-year variability in OpenMARS. Maps of standard deviations indicate a depression between the main peaks in Tharsis and higher variability in Mare Boreum than Mare Australe. The main conclusion is that the atmosphere of Mars routinely operates in the compressible regime (0.3 < Ma < 0.8), unlike Earth. Aspects of all flow-speed meteorology are discussed.

Enhancing geostatistical prediction of total hydrocarbon content (THC) using Bayesian maximum entropy (BME) in a region of Niger Delta, Nigeria

In this study, Bayesian maximum entropy (BME) was implemented to take advantage of the limited data available for reliable prediction of total hydrocarbon content (THC) in the soil because it allows assimilate of secondary and its uncertainty data. Thirteen (13) physico-chemical soil variables were obtained. Upon the application of multiple regression analysis to the soil variables, electrical conductivity, EC was found as a covariate to the eleven (11) data. Twenty-eight (28) data were then generated by regression model between and EC and their 28 uncertainty data were also derived using probability density function. Therefore, 11 , 28 and 28 error data in the computation of THC soft were integrated at BME technique to produce BME prediction and BME standard deviation maps of THC. The BME predicted THC value in the area ranges from 0 – 16 . The BME prediction map provides the mean variable of the estimation posterior pdf of THC. It shows the spatial variability of THC as the concentration decreases from the highest concentrated zone of THC.

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with 81m Kr-gas SPECT ventilation imaging.

PurposeThis study aimed to evaluate the accuracy of deep learning (DL)‐based computed tomography (CT) ventilation imaging (CTVI).MethodsA total of 71 cases that underwent single‐photon emission CT 81mKr‐gas ventilation (SPECT V) and CT imaging were included. Sixty cases were assigned to the training and validation sets, and the remaining 11 cases were assigned to the test set. To directly transform three‐dimensional (3D) CT (free‐breathing CT) images to SPECT V images, a DL‐based model was implemented based on the U‐Net architecture. The input and output data were 3DCT‐ and SPECT V‐masked, respectively, except for whole‐lung volumes. These data were rearranged in voxel size, registered rigidly, cropped, and normalized in preprocessing. In addition to a standard estimation method (i.e., without dropout during the estimation process), a Monte Carlo dropout (MCD) method (i.e., with dropout during the estimation process) was used to calculate prediction uncertainty. To evaluate the two models’ (CTVIMCD U‐Net, CTVIU‐Net) performance, we used fivefold cross‐validation for the training and validation sets. To test the final model performances for both approaches, we applied the test set to each trained model and averaged the test prediction results from the five trained models to acquire the mean test result (bagging) for each approach. For the MCD method, the models were predicted repeatedly (sample size = 200), and the average and standard deviation (SD) maps were calculated in each voxel from the predicted results: The average maps were defined as test prediction results in each fold. As an evaluation index, the voxel‐wise Spearman rank correlation coefficient (Spearman r s) and Dice similarity coefficient (DSC) were calculated. The DSC was calculated for three functional regions (high, moderate, and low) separated by an almost equal volume. The coefficient of variation was defined as prediction uncertainty, and these average values were calculated within three functional regions. The Wilcoxon signed‐rank test was used to test for a significant difference between the two DL‐based approaches.ResultsThe average indexes with one SD (1SD) between CTVIMCD U‐Net and SPECT V were 0.76 ± 0.06, 0.69 ± 0.07, 0.51 ± 0.06, and 0.75 ± 0.04 for Spearman r s, DSChigh, DSCmoderate, and DSClow, respectively. The average indexes with 1SD between CTVIU‐Net and SPECT V were 0.72 ± 0.05, 0.66 ± 0.04, 0.48 ± 0.04, and 0.74 ± 0.06 for Spearman r s, DSChigh, DSCmoderate, and DSClow, respectively. These indexes between CTVIMCD U‐Net and CTVIU‐Net showed no significance difference (Spearman r s, p = 0.175; DSChigh, p = 0.123; DSCmoderate, p = 0.278; DSClow, p = 0.520). The average coefficient of variations with 1SD were 0.27 ± 0.00, 0.27 ± 0.01, and 0.36 ± 0.03 for the high‐, moderate‐, and low‐functional regions, respectively, and the low‐functional region showed a tendency to exhibit larger uncertainties than the others.ConclusionWe evaluated DL‐based framework for estimating lung‐functional ventilation images only from CT images. The results indicated that the DL‐based approach could potentially be used for lung‐ventilation estimation.

Determination of the electronic portal imaging device pixel-sensitivity-map for quality assurance applications. Part 1: Comparison of methods.

PurposeCalibration of a radiotherapy electronic portal imaging device (EPID) using the pixel‐sensitivity‐map (PSM) in place of the flood field correction improves the utility of the EPID for quality assurance applications. Multiple methods are available for determining the PSM and this study provides an evaluation to inform on which is superior.MethodsThree different empirical methods (“Calvary Mater Newcastle” [CMN], “Varian,” and “WashU”) and a Monte Carlo‐based method of PSM determination were investigated on a single Varian TrueBeam STx linear accelerator (linac) with an aS1200 EPID panel. PSM measurements were performed for each empirical method three successive times using the 6 MV beam. The resulting PSM from each method was compared to the Monte Carlo method as a reference using 2D percentage deviation maps and histograms plus crossplane profiles. The repeatability of generated PSMs was also assessed via 2D standard deviation (SD) maps and histograms. Additionally, the Beam‐Response generated by removal of the PSM from a raw EPID image for each method was visually contrasted. Finally, the practicality of each method was assessed qualitatively and via the measured time required to acquire and export the required images.ResultsThe median pixel‐by‐pixel percentage deviation between each of the empirical PSM methods and the Monte Carlo PSM was ‐0.36%, 0.24%, and 0.74% for the CMN, Varian, and WashU methods, respectively. Ninety‐five percent of pixels were found to be repeatable to within ‐0.21%, 0.08%, 0.19%, and 0.35% (1 SD) for the CMN, Monte Carlo, Varian, and WashU methods, respectively. The WashU method was found to be quickest for data acquisition and export and the CMN the slowest.ConclusionFor the first time four methods of generating the EPID PSM have been compared in detail and strengths and weaknesses of each method have been identified. All methods are considered likely to be clinically acceptable and with similar practical requirements.

Building Height Extraction from GF-7 Satellite Images Based on Roof Contour Constrained Stereo Matching

Building height is one of the basic geographic information for planning and analysis in urban construction. It is still very challenging to estimate the accurate height of complex buildings from satellite images, especially for buildings with podium. This paper proposes a solution for building height estimation from GF-7 satellite images by using a roof contour constrained stereo matching algorithm and DSM (Digital Surface Model) based bottom elevation estimation. First, an object-oriented roof matching algorithm is proposed based on building contour to extract accurate building roof elevation from GF-7 stereo image, and DSM generated from the GF-7 stereo images is then used to obtain building bottom elevation. Second, roof contour constrained stereo matching is conducted between backward and forward image blocks, in which the difference of standard deviation maps is used for the similarity measure. To deal with the multi-height problem of podium buildings, the gray difference image is adopted to segment podium buildings, and re-matching is conducted to find out their actual heights. Third, the building height is obtained through the elevation difference between the building top and bottom, in which the evaluation of the building bottom is calculated according to the elevation histogram statistics of the building buffer in DSM. Finally, two GF-7 stereo satellite images, collected in Yingde, Guangzhou, and Xi’an, Shanxi, are used for performance evaluation. Besides, the aerial LiDAR point cloud is used for absolute accuracy evaluation. The results demonstrate that compared with other methods, our solution obviously improves the accuracy of height estimation of high-rise buildings. The MAE (Mean Absolute Error) of the estimated building heights in Yingde is 2.31 m, and the MAE of the estimated elevation of building top and bottom is approximately 1.57 m and 1.91 m, respectively. Then the RMSE (Root Mean Square Error) of building top and bottom is 2.01 m and 2.57 m. As for the Xi’an dataset with 7 buildings with podium out of 40 buildings, the MAE of the estimated building height is 1.69 m and the RMSE is 2.34 m. The proposed method can be an effective solution for building height extraction from GF-7 satellite images.

Towards an image quality criterion to optimize Digital image correlation. Use of an analytical model to optimize acquisition conditions

Digital image correlation (DIC) is expanding in many fields. In particular, it requires that the acquisition conditions and speckle be adapted to each application. A new methodology allowing to define the quality of the images in a fast and precise way is proposed. An analytical model based on mean intensity gradient of speckle pattern criterion and the standard deviation of displacement on images has been proposed. The model can be used on the whole image or by creating subsets in the image. Standard deviation maps of displacement are obtained. The methodology makes it possible to save a considerable amount of time compared to the full DIC calculation to optimize the image acquisition conditions, the speckle and the DIC parameters.

Bayesian slip inversion with automatic differentiation variational inference

SUMMARYThe Bayesian slip inversion offers a powerful tool for modelling the earthquake source mechanism. It can provide a fully probabilistic result and thus permits us to quantitatively assess the inversion uncertainty. The Bayesian problem is usually solved with Monte Carlo methods, but they are computationally expensive and are inapplicable for high-dimensional and large-scale problems. Variational inference is an alternative solver to the Bayesian problem. It turns Bayesian inference into an optimization task and thus enjoys better computational performances. In this study, we introduce a general variational inference algorithm, automatic differentiation variational inference (ADVI), to the Bayesian slip inversion and compare it with the classic Metropolis–Hastings (MH) sampling method. The synthetic test shows that the two methods generate nearly identical mean slip distributions and standard deviation maps. In the real case study, the two methods produce highly consistent mean slip distributions, but the ADVI-derived standard deviation map differs from that produced by the MH method, possibly because of the limitation of the Gaussian approximation in the ADVI method. In both cases, ADVI can give comparable results to the MH method but with a significantly lower computational cost. Our results show that ADVI is a promising and competitive method for the Bayesian slip inversion.